| Summary: | Thesis (MEng)--Stellenbosch University, 2014. === ENGLISH ABSTRACT: Machine learning techniques have been around for a few decades now and
are being established as a pre-dominant feature in most control applications.
Elevators create a unique control application where traffic flow is controlled
and directed according to certain control philosophies. Machine learning techniques
can be implemented to predict and control every possible traffic flow
scenario and deliver the best possible solution. Various techniques will be implemented
in the elevator application in an attempt to establish a degree of
artificial intelligence in the decision making process and to be able to have
increased interaction with the passengers at all times.
The primary objective for this thesis is to investigate the potential of machine
learning solutions and the relevancy of such technologies in elevator control
applications. The aim is to establish how the research field of machine learning,
specifically neural network science, can be successfully utilised with the
goal of creating an artificial intelligent (AI) controller. The AI controller is
to adapt to its existing state and change its control parameters as required
without the intervention of the user.
The secondary objective for this thesis is to develop an elevator model that represents
every aspect of the real-world application. The purpose of the model
is to improve the accuracy of existing theoretical and simulated models, by
modulating previously unknown and complex variables and constraints. The
aim is to create a complete and fully functional testing platform for developing
new elevator control philosophies and testing new elevator control mechanisms. To achieve these objectives, the main focus is directed to how waiting time,
probability theory and power consumption predictions can be optimally utilised
by means of machine learning solutions. The theoretical background is provided
for these concepts and how each subject can potentially influence the
decision making process. The reason why this approach has been difficult to
implement in the past, is possibly mainly due to the lack of adequate representation
for these concepts in an online environment without the continuous
feedback from an Expert System. As a result of this thesis, the respective
online models for each of these concepts were successfully developed in order
to deal with the identified shortcomings.
The developed online models for projected waiting times, probability networks
and power consumption feedback were then combined to form a new Intelligent
Elevator Controller (IEC) structure as opposed to the Expert System
approach, mostly used in present computer based elevator controllers. === AFRIKAANSE OPSOMMING: Masjienleertegnieke bestaan al vir 'n paar dekades en is 'n oorwegende kenmerk
in hedendaagse beheertoestelle. Hysbakke skep 'n unieke beheertoepassing,
waar verkeersvloei beheer en gerig kan word volgens sekere beheer loso e.
Masjienleertegnieke kan geïmplementeer word om elke moontlike verkeersvloei
situasie te voorspel en te beheer en die beste moontlike oplossing te lewer.
Verskeie tegnieke sal in die tesis ondersoek word in 'n poging om 'n mate van
kunsmatige intelligensie in die besluitneming proses te skep asook verhoogte
interaksie met die passasiers te alle tye.
Die prim^ere doel van hierdie tesis is om die potensiaal van 'n masjienleer oplossing
en die toepaslikheid van dit in hysbakbeheertoepassings te ondersoek.
Die doel is om vas te stel hoe die navorsing in die veld van die masjienleer,
spesi ek in neurale netwerk wetenskappe, suksesvol aangewend kan word met
die doel om 'n kunsmatige intelligente beheerder te skep. Die kunsmatige intelligente
beheerder moet kan aanpas by sy onmidelike omgewing en sy beheer
parameters moet kan verander soos nodig sonder die ingryping van die gebruiker.
Die sekond^ere doelwit vir hierdie tesis is om 'n hysbakmodel, wat elke aspek
van die werklike w^ereld verteenwoordig, te ontwikkel. Die doel van die
model is om die akkuraatheid van die bestaande teoretiese en gesimuleerde
modelle te verbeter deur voorheen onbekende en komplekse veranderlikes en
beperkings in ag te neem. Die doel is om 'n funksionele toetsplatform te skep vir die ontwikkeling van
nuwe hysbakbeheer loso e en vir die toets van nuwe hysbakbeheermeganismes.
Om hierdie doelwitte te bereik, is die hoo okus gerig om wagtyd, waarskynlikheidsteorie
en kragverbruik voorspellings optimaal te gebruik deur middel
van die masjienleer oplossings. Die teoretiese agtergrond is voorsien vir hierdie
konsepte en hoe elke konsep potensieel die besluitneming kan beïnvloed. Die
rede waarom hierdie benadering moeilik was om te implementeer tot hede, is
moontlik te wyte aan die gebrek aan voldoende verteenwoordiging vir hierdie
konsepte in 'n aanlynomgewing sonder die voortdurende terugvoer van 'n Deskundige
Stelsel. As gevolg van hierdie tesis word die onderskeie aanlynmodelle
vir elk van hierdie konsepte suksesvol ontwikkel om die geïdenti seerde tekortkominge
te oorkom.
Die ontwikkelde aanlynmodelle vir geprojekteerde wagtye, waarskynlikheidsnetwerke
en kragverbruik terugvoer is dan gekombineer om 'n nuwe intelligente
hysbakbeheerder struktuur te skep, in teenstelling met die Deskundige Stelsel
benadering in die huidige rekenaar gebaseerde hysbakbeheerders.
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